pcal (version 1.0.0)

bfactor_log_interpret_kr: Interpretation of the logarithms of Bayes factors

Description

Quantify the strength of the evidence provided by the data to a model/hypothesis according a Bayes factor interpretation scale suggested by kass1995;textualpcal.

Usage

bfactor_log_interpret_kr(bf, base = exp(1))

Arguments

bf

A numeric vector.

base

A numeric vector of length one. Must be a positive number.

Value

Returns a character vector with the same length as bf.

Details

Bayes factors are a summary of the evidence provided by the data to a model/hypothesis, and are often reported on a logarithmic scale. Because it can be useful to consider twice the natural logarithm of the Bayes factor, which is in the same scale as the familiar deviance and likelihood ratio test statistics, kass1995;textualpcal suggested the following Bayes factor interpretation scale:

2*log(Bayes factor)	Bayes factor	Evidence
[-Inf, 0[	[0, 1[	Negative
[0, 2[	[1, 3[	Weak
[2, 6[	[3, 20[	Positive
[6, 10[	[20, 150[	Strong
[10, +Inf[	[150, +Inf[	Very strong

bfactor_log_interpret_kr takes (base base) logarithms of Bayes factors as input and returns the strength of the evidence provided by the data in favor of the model/hypothesis in the numerator of the Bayes factors (usually the null hypothesis) according to the aforementioned table.

When comparing results with those from standard likelihood ratio tests, it is convenient to put the null hypothesis in the denominator of the Bayes factor so that bfactor_log_interpret_kr returns the strength of the evidence against the null hypothesis. If bf was obtained with the null hypothesis on the numerator, one can use bfactor_log_interpret_kr(1/bf) to obtain the strength of the evidence against the null hypothesis.

References

See Also

• bfactor_log_interpret for the original interpretation scale suggested by Harold Jeffreys

• bfactor_interpret and bfactor_interpret_kr for the interpretation of Bayes factors in levels.

• bfactor_to_prob to turn Bayes factors into posterior probabilities.

• bcal for a p-value calibration that returns lower bounds on Bayes factors in favor of point null hypotheses.

Examples

# NOT RUN {
# Interpretation of one Bayes factor, natural log
bfactor_log_interpret_kr(1.5)

# Interpretation of many Bayes factors, natural log
bfactor_log_interpret_kr(c(0.1, 1.2, 3.5, 13.9, 150))

# Interpretation of many Bayes factors, base 10 log
bfactor_log_interpret_kr(log10(c(0.1, 1.2, 3.5, 13.9, 150)), base = 10)

# }